Overview

Dataset statistics

Number of variables40
Number of observations700
Missing cells760
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory998.6 KiB
Average record size in memory1.4 KiB

Variable types

Numeric14
DateTime2
Categorical22
Text1
Unsupported1

Alerts

age is highly overall correlated with months_as_customerHigh correlation
auto_make is highly overall correlated with auto_modelHigh correlation
auto_model is highly overall correlated with auto_makeHigh correlation
collision_type is highly overall correlated with incident_type and 4 other fieldsHigh correlation
incident_type is highly overall correlated with collision_type and 5 other fieldsHigh correlation
injury_claim is highly overall correlated with collision_type and 4 other fieldsHigh correlation
months_as_customer is highly overall correlated with ageHigh correlation
number_of_vehicles_involved is highly overall correlated with incident_typeHigh correlation
property_claim is highly overall correlated with collision_type and 4 other fieldsHigh correlation
total_claim_amount is highly overall correlated with collision_type and 4 other fieldsHigh correlation
vehicle_claim is highly overall correlated with collision_type and 4 other fieldsHigh correlation
authorities_contacted has 60 (8.6%) missing valuesMissing
_c39 has 700 (100.0%) missing valuesMissing
policy_number has unique valuesUnique
incident_location has unique valuesUnique
_c39 is an unsupported type, check if it needs cleaning or further analysisUnsupported
umbrella_limit has 557 (79.6%) zerosZeros
capital-gains has 347 (49.6%) zerosZeros
capital-loss has 343 (49.0%) zerosZeros
incident_hour_of_the_day has 35 (5.0%) zerosZeros
injury_claim has 19 (2.7%) zerosZeros
property_claim has 14 (2.0%) zerosZeros

Reproduction

Analysis started2026-02-26 07:59:02.706467
Analysis finished2026-02-26 07:59:24.697433
Duration21.99 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

months_as_customer
Real number (ℝ)

High correlation 

Distinct336
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.89429
Minimum0
Maximum479
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:24.770113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28.9
Q1114
median202
Q3279
95-th percentile431.2
Maximum479
Range479
Interquartile range (IQR)165

Descriptive statistics

Standard deviation116.01871
Coefficient of variation (CV)0.56348678
Kurtosis-0.50243659
Mean205.89429
Median Absolute Deviation (MAD)82
Skewness0.36509398
Sum144126
Variance13460.341
MonotonicityNot monotonic
2026-02-26T15:59:24.890446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2106
 
0.9%
1406
 
0.9%
2896
 
0.9%
2596
 
0.9%
2306
 
0.9%
2455
 
0.7%
1075
 
0.7%
1285
 
0.7%
2545
 
0.7%
1565
 
0.7%
Other values (326)645
92.1%
ValueCountFrequency (%)
01
 
0.1%
13
0.4%
22
0.3%
31
 
0.1%
42
0.3%
51
 
0.1%
83
0.4%
91
 
0.1%
101
 
0.1%
111
 
0.1%
ValueCountFrequency (%)
4791
0.1%
4782
0.3%
4752
0.3%
4731
0.1%
4721
0.1%
4671
0.1%
4651
0.1%
4641
0.1%
4631
0.1%
4612
0.3%

age
Real number (ℝ)

High correlation 

Distinct45
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.105714
Minimum20
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:25.001911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26
Q132
median38
Q345
95-th percentile57
Maximum64
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1662578
Coefficient of variation (CV)0.23439689
Kurtosis-0.31860974
Mean39.105714
Median Absolute Deviation (MAD)6
Skewness0.52929296
Sum27374
Variance84.020282
MonotonicityNot monotonic
2026-02-26T15:59:25.113113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
3437
 
5.3%
4333
 
4.7%
3332
 
4.6%
4131
 
4.4%
3129
 
4.1%
3929
 
4.1%
3028
 
4.0%
3228
 
4.0%
3727
 
3.9%
3826
 
3.7%
Other values (35)400
57.1%
ValueCountFrequency (%)
201
 
0.1%
211
 
0.1%
221
 
0.1%
233
 
0.4%
243
 
0.4%
2511
1.6%
2622
3.1%
2720
2.9%
2817
2.4%
2926
3.7%
ValueCountFrequency (%)
642
 
0.3%
632
 
0.3%
623
 
0.4%
616
0.9%
605
 
0.7%
593
 
0.4%
587
1.0%
5714
2.0%
567
1.0%
559
1.3%

policy_number
Real number (ℝ)

Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean549242.08
Minimum100804
Maximum999435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:25.224818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100804
5-th percentile139393.55
Q1343282.75
median536799
Q3757918
95-th percentile960775.55
Maximum999435
Range898631
Interquartile range (IQR)414635.25

Descriptive statistics

Standard deviation258480.46
Coefficient of variation (CV)0.470613
Kurtosis-1.1114084
Mean549242.08
Median Absolute Deviation (MAD)206605.5
Skewness0.022542976
Sum3.8446945 × 108
Variance6.681215 × 1010
MonotonicityNot monotonic
2026-02-26T15:59:25.339872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5962981
 
0.1%
1255911
 
0.1%
9677131
 
0.1%
6490821
 
0.1%
5193121
 
0.1%
1905881
 
0.1%
6701421
 
0.1%
7516121
 
0.1%
2374181
 
0.1%
3150411
 
0.1%
Other values (690)690
98.6%
ValueCountFrequency (%)
1008041
0.1%
1014211
0.1%
1045941
0.1%
1061861
0.1%
1071811
0.1%
1088441
0.1%
1093921
0.1%
1100841
0.1%
1101221
0.1%
1101431
0.1%
ValueCountFrequency (%)
9994351
0.1%
9988651
0.1%
9981921
0.1%
9968501
0.1%
9962531
0.1%
9945381
0.1%
9938401
0.1%
9921451
0.1%
9915531
0.1%
9914801
0.1%
Distinct673
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum1990-01-08 00:00:00
Maximum2015-02-22 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-26T15:59:25.455138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:25.584714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

policy_state
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size40.5 KiB
OH
249 
IL
237 
IN
214 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1400
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIN
2nd rowIL
3rd rowIL
4th rowOH
5th rowOH

Common Values

ValueCountFrequency (%)
OH249
35.6%
IL237
33.9%
IN214
30.6%

Length

2026-02-26T15:59:25.706932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:25.783906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
oh249
35.6%
il237
33.9%
in214
30.6%

Most occurring characters

ValueCountFrequency (%)
I451
32.2%
O249
17.8%
H249
17.8%
L237
16.9%
N214
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I451
32.2%
O249
17.8%
H249
17.8%
L237
16.9%
N214
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I451
32.2%
O249
17.8%
H249
17.8%
L237
16.9%
N214
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I451
32.2%
O249
17.8%
H249
17.8%
L237
16.9%
N214
15.3%

policy_csl
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
100/300
259 
250/500
248 
500/1000
193 

Length

Max length8
Median length7
Mean length7.2757143
Min length7

Characters and Unicode

Total characters5093
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500/1000
2nd row250/500
3rd row500/1000
4th row500/1000
5th row100/300

Common Values

ValueCountFrequency (%)
100/300259
37.0%
250/500248
35.4%
500/1000193
27.6%

Length

2026-02-26T15:59:25.862578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:25.922323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
100/300259
37.0%
250/500248
35.4%
500/1000193
27.6%

Most occurring characters

ValueCountFrequency (%)
02745
53.9%
/700
 
13.7%
5689
 
13.5%
1452
 
8.9%
3259
 
5.1%
2248
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)5093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02745
53.9%
/700
 
13.7%
5689
 
13.5%
1452
 
8.9%
3259
 
5.1%
2248
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02745
53.9%
/700
 
13.7%
5689
 
13.5%
1452
 
8.9%
3259
 
5.1%
2248
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02745
53.9%
/700
 
13.7%
5689
 
13.5%
1452
 
8.9%
3259
 
5.1%
2248
 
4.9%

policy_deductable
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size41.6 KiB
1000
250 
500
231 
2000
219 

Length

Max length4
Median length4
Mean length3.67
Min length3

Characters and Unicode

Total characters2569
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row500
3rd row1000
4th row500
5th row1000

Common Values

ValueCountFrequency (%)
1000250
35.7%
500231
33.0%
2000219
31.3%

Length

2026-02-26T15:59:26.000056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:26.057667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1000250
35.7%
500231
33.0%
2000219
31.3%

Most occurring characters

ValueCountFrequency (%)
01869
72.8%
1250
 
9.7%
5231
 
9.0%
2219
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01869
72.8%
1250
 
9.7%
5231
 
9.0%
2219
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01869
72.8%
1250
 
9.7%
5231
 
9.0%
2219
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01869
72.8%
1250
 
9.7%
5231
 
9.0%
2219
 
8.5%

policy_annual_premium
Real number (ℝ)

Distinct694
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1245.9763
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:26.145827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile836.0555
Q11077.46
median1252.28
Q31402.7575
95-th percentile1633.37
Maximum2047.59
Range1614.26
Interquartile range (IQR)325.2975

Descriptive statistics

Standard deviation248.3109
Coefficient of variation (CV)0.19929023
Kurtosis0.15696392
Mean1245.9763
Median Absolute Deviation (MAD)168.6
Skewness0.026410046
Sum872183.39
Variance61658.303
MonotonicityNot monotonic
2026-02-26T15:59:26.384044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1074.072
 
0.3%
1389.132
 
0.3%
1558.292
 
0.3%
1073.832
 
0.3%
1362.872
 
0.3%
1281.252
 
0.3%
1238.651
 
0.1%
1464.731
 
0.1%
1330.461
 
0.1%
796.351
 
0.1%
Other values (684)684
97.7%
ValueCountFrequency (%)
433.331
0.1%
484.671
0.1%
538.171
0.1%
566.111
0.1%
617.111
0.1%
664.861
0.1%
671.011
0.1%
671.921
0.1%
694.451
0.1%
709.141
0.1%
ValueCountFrequency (%)
2047.591
0.1%
1935.851
0.1%
1927.871
0.1%
1922.841
0.1%
1896.911
0.1%
1878.441
0.1%
1865.831
0.1%
1863.041
0.1%
1861.431
0.1%
1851.781
0.1%

umbrella_limit
Real number (ℝ)

Zeros 

Distinct11
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100000
Minimum-1000000
Maximum10000000
Zeros557
Zeros (%)79.6%
Negative1
Negative (%)0.1%
Memory size5.6 KiB
2026-02-26T15:59:26.474135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range11000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2282921.6
Coefficient of variation (CV)2.0753833
Kurtosis1.6657313
Mean1100000
Median Absolute Deviation (MAD)0
Skewness1.7822911
Sum7.7 × 108
Variance5.211731 × 1012
MonotonicityNot monotonic
2026-02-26T15:59:26.554259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0557
79.6%
600000038
 
5.4%
500000034
 
4.9%
400000028
 
4.0%
700000021
 
3.0%
30000008
 
1.1%
80000007
 
1.0%
20000003
 
0.4%
90000002
 
0.3%
100000001
 
0.1%
ValueCountFrequency (%)
-10000001
 
0.1%
0557
79.6%
20000003
 
0.4%
30000008
 
1.1%
400000028
 
4.0%
500000034
 
4.9%
600000038
 
5.4%
700000021
 
3.0%
80000007
 
1.0%
90000002
 
0.3%
ValueCountFrequency (%)
100000001
 
0.1%
90000002
 
0.3%
80000007
 
1.0%
700000021
 
3.0%
600000038
 
5.4%
500000034
 
4.9%
400000028
 
4.0%
30000008
 
1.1%
20000003
 
0.4%
0557
79.6%

insured_zip
Real number (ℝ)

Distinct699
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501432.61
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:26.651634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433246.7
Q1447978.25
median466242
Q3603946
95-th percentile617701
Maximum620962
Range190858
Interquartile range (IQR)155967.75

Descriptive statistics

Standard deviation72210.252
Coefficient of variation (CV)0.14400789
Kurtosis-1.2077346
Mean501432.61
Median Absolute Deviation (MAD)22351.5
Skewness0.80913098
Sum3.5100283 × 108
Variance5.2143204 × 109
MonotonicityNot monotonic
2026-02-26T15:59:26.768770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4312022
 
0.3%
4355521
 
0.1%
4509471
 
0.1%
6002081
 
0.1%
4312771
 
0.1%
4354891
 
0.1%
6141661
 
0.1%
4741671
 
0.1%
4438611
 
0.1%
4415361
 
0.1%
Other values (689)689
98.4%
ValueCountFrequency (%)
4301041
0.1%
4301411
0.1%
4302321
0.1%
4303801
0.1%
4306211
0.1%
4308321
0.1%
4308531
0.1%
4308781
0.1%
4308861
0.1%
4309871
0.1%
ValueCountFrequency (%)
6209621
0.1%
6208191
0.1%
6207571
0.1%
6205071
0.1%
6203581
0.1%
6202071
0.1%
6201971
0.1%
6198921
0.1%
6198841
0.1%
6198111
0.1%

insured_sex
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
FEMALE
370 
MALE
330 

Length

Max length6
Median length6
Mean length5.0571429
Min length4

Characters and Unicode

Total characters3540
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFEMALE
2nd rowMALE
3rd rowFEMALE
4th rowMALE
5th rowFEMALE

Common Values

ValueCountFrequency (%)
FEMALE370
52.9%
MALE330
47.1%

Length

2026-02-26T15:59:26.892079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:26.955719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female370
52.9%
male330
47.1%

Most occurring characters

ValueCountFrequency (%)
E1070
30.2%
A700
19.8%
M700
19.8%
L700
19.8%
F370
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E1070
30.2%
A700
19.8%
M700
19.8%
L700
19.8%
F370
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E1070
30.2%
A700
19.8%
M700
19.8%
L700
19.8%
F370
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E1070
30.2%
A700
19.8%
M700
19.8%
L700
19.8%
F370
 
10.5%
Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size43.1 KiB
JD
120 
High School
105 
Associate
105 
Masters
99 
MD
93 
Other values (2)
178 

Length

Max length11
Median length9
Mean length5.8814286
Min length2

Characters and Unicode

Total characters4117
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMasters
2nd rowJD
3rd rowHigh School
4th rowJD
5th rowMD

Common Values

ValueCountFrequency (%)
JD120
17.1%
High School105
15.0%
Associate105
15.0%
Masters99
14.1%
MD93
13.3%
College91
13.0%
PhD87
12.4%

Length

2026-02-26T15:59:27.030781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:27.112536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
jd120
14.9%
high105
13.0%
school105
13.0%
associate105
13.0%
masters99
12.3%
md93
11.6%
college91
11.3%
phd87
10.8%

Most occurring characters

ValueCountFrequency (%)
s408
 
9.9%
o406
 
9.9%
e386
 
9.4%
D300
 
7.3%
h297
 
7.2%
l287
 
7.0%
i210
 
5.1%
c210
 
5.1%
t204
 
5.0%
a204
 
5.0%
Other values (10)1205
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4117
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s408
 
9.9%
o406
 
9.9%
e386
 
9.4%
D300
 
7.3%
h297
 
7.2%
l287
 
7.0%
i210
 
5.1%
c210
 
5.1%
t204
 
5.0%
a204
 
5.0%
Other values (10)1205
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4117
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s408
 
9.9%
o406
 
9.9%
e386
 
9.4%
D300
 
7.3%
h297
 
7.2%
l287
 
7.0%
i210
 
5.1%
c210
 
5.1%
t204
 
5.0%
a204
 
5.0%
Other values (10)1205
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4117
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s408
 
9.9%
o406
 
9.9%
e386
 
9.4%
D300
 
7.3%
h297
 
7.2%
l287
 
7.0%
i210
 
5.1%
c210
 
5.1%
t204
 
5.0%
a204
 
5.0%
Other values (10)1205
29.3%

insured_occupation
Categorical

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size48.3 KiB
machine-op-inspct
66 
exec-managerial
57 
craft-repair
54 
sales
54 
prof-specialty
53 
Other values (9)
416 

Length

Max length17
Median length16
Mean length13.53
Min length5

Characters and Unicode

Total characters9471
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprotective-serv
2nd rowcraft-repair
3rd rowmachine-op-inspct
4th rowtransport-moving
5th rowcraft-repair

Common Values

ValueCountFrequency (%)
machine-op-inspct66
 
9.4%
exec-managerial57
 
8.1%
craft-repair54
 
7.7%
sales54
 
7.7%
prof-specialty53
 
7.6%
priv-house-serv52
 
7.4%
armed-forces51
 
7.3%
tech-support50
 
7.1%
transport-moving50
 
7.1%
other-service47
 
6.7%
Other values (4)166
23.7%

Length

2026-02-26T15:59:27.227376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct66
 
9.4%
exec-managerial57
 
8.1%
craft-repair54
 
7.7%
sales54
 
7.7%
prof-specialty53
 
7.6%
priv-house-serv52
 
7.4%
armed-forces51
 
7.3%
tech-support50
 
7.1%
transport-moving50
 
7.1%
other-service47
 
6.7%
Other values (4)166
23.7%

Most occurring characters

ValueCountFrequency (%)
e1077
11.4%
r967
 
10.2%
-764
 
8.1%
a761
 
8.0%
s688
 
7.3%
i657
 
6.9%
c613
 
6.5%
p535
 
5.6%
t502
 
5.3%
o460
 
4.9%
Other values (11)2447
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9471
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1077
11.4%
r967
 
10.2%
-764
 
8.1%
a761
 
8.0%
s688
 
7.3%
i657
 
6.9%
c613
 
6.5%
p535
 
5.6%
t502
 
5.3%
o460
 
4.9%
Other values (11)2447
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9471
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1077
11.4%
r967
 
10.2%
-764
 
8.1%
a761
 
8.0%
s688
 
7.3%
i657
 
6.9%
c613
 
6.5%
p535
 
5.6%
t502
 
5.3%
o460
 
4.9%
Other values (11)2447
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9471
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1077
11.4%
r967
 
10.2%
-764
 
8.1%
a761
 
8.0%
s688
 
7.3%
i657
 
6.9%
c613
 
6.5%
p535
 
5.6%
t502
 
5.3%
o460
 
4.9%
Other values (11)2447
25.8%

insured_hobbies
Categorical

Distinct20
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
exercise
 
45
bungie-jumping
 
44
golf
 
42
polo
 
41
movies
 
41
Other values (15)
487 

Length

Max length14
Median length10
Mean length8.0371429
Min length4

Characters and Unicode

Total characters5626
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreading
2nd rowpolo
3rd rowskydiving
4th rowvideo-games
5th rowvideo-games

Common Values

ValueCountFrequency (%)
exercise45
 
6.4%
bungie-jumping44
 
6.3%
golf42
 
6.0%
polo41
 
5.9%
movies41
 
5.9%
paintball40
 
5.7%
reading38
 
5.4%
kayaking38
 
5.4%
yachting38
 
5.4%
camping38
 
5.4%
Other values (10)295
42.1%

Length

2026-02-26T15:59:27.324931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
exercise45
 
6.4%
bungie-jumping44
 
6.3%
golf42
 
6.0%
polo41
 
5.9%
movies41
 
5.9%
paintball40
 
5.7%
reading38
 
5.4%
kayaking38
 
5.4%
yachting38
 
5.4%
camping38
 
5.4%
Other values (10)295
42.1%

Most occurring characters

ValueCountFrequency (%)
i644
 
11.4%
g496
 
8.8%
e488
 
8.7%
n465
 
8.3%
a459
 
8.2%
s379
 
6.7%
o248
 
4.4%
l231
 
4.1%
p228
 
4.1%
m216
 
3.8%
Other values (14)1772
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)5626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i644
 
11.4%
g496
 
8.8%
e488
 
8.7%
n465
 
8.3%
a459
 
8.2%
s379
 
6.7%
o248
 
4.4%
l231
 
4.1%
p228
 
4.1%
m216
 
3.8%
Other values (14)1772
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i644
 
11.4%
g496
 
8.8%
e488
 
8.7%
n465
 
8.3%
a459
 
8.2%
s379
 
6.7%
o248
 
4.4%
l231
 
4.1%
p228
 
4.1%
m216
 
3.8%
Other values (14)1772
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i644
 
11.4%
g496
 
8.8%
e488
 
8.7%
n465
 
8.3%
a459
 
8.2%
s379
 
6.7%
o248
 
4.4%
l231
 
4.1%
p228
 
4.1%
m216
 
3.8%
Other values (14)1772
31.5%
Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size45.6 KiB
own-child
130 
other-relative
126 
husband
125 
not-in-family
123 
wife
105 

Length

Max length14
Median length9
Mean length9.4957143
Min length4

Characters and Unicode

Total characters6647
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot-in-family
2nd rowother-relative
3rd rowwife
4th rowown-child
5th rowown-child

Common Values

ValueCountFrequency (%)
own-child130
18.6%
other-relative126
18.0%
husband125
17.9%
not-in-family123
17.6%
wife105
15.0%
unmarried91
13.0%

Length

2026-02-26T15:59:27.417359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:27.493628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
own-child130
18.6%
other-relative126
18.0%
husband125
17.9%
not-in-family123
17.6%
wife105
15.0%
unmarried91
13.0%

Most occurring characters

ValueCountFrequency (%)
i698
 
10.5%
n592
 
8.9%
e574
 
8.6%
-502
 
7.6%
a465
 
7.0%
r434
 
6.5%
h381
 
5.7%
o379
 
5.7%
l379
 
5.7%
t375
 
5.6%
Other values (10)1868
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)6647
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i698
 
10.5%
n592
 
8.9%
e574
 
8.6%
-502
 
7.6%
a465
 
7.0%
r434
 
6.5%
h381
 
5.7%
o379
 
5.7%
l379
 
5.7%
t375
 
5.6%
Other values (10)1868
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6647
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i698
 
10.5%
n592
 
8.9%
e574
 
8.6%
-502
 
7.6%
a465
 
7.0%
r434
 
6.5%
h381
 
5.7%
o379
 
5.7%
l379
 
5.7%
t375
 
5.6%
Other values (10)1868
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6647
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i698
 
10.5%
n592
 
8.9%
e574
 
8.6%
-502
 
7.6%
a465
 
7.0%
r434
 
6.5%
h381
 
5.7%
o379
 
5.7%
l379
 
5.7%
t375
 
5.6%
Other values (10)1868
28.1%

capital-gains
Real number (ℝ)

Zeros 

Distinct270
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25804.857
Minimum0
Maximum100500
Zeros347
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:27.612187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11550
Q351400
95-th percentile70915
Maximum100500
Range100500
Interquartile range (IQR)51400

Descriptive statistics

Standard deviation28043.4
Coefficient of variation (CV)1.0867489
Kurtosis-1.3287833
Mean25804.857
Median Absolute Deviation (MAD)11550
Skewness0.43786012
Sum18063400
Variance7.8643231 × 108
MonotonicityNot monotonic
2026-02-26T15:59:27.735851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0347
49.6%
515004
 
0.6%
463004
 
0.6%
514003
 
0.4%
532003
 
0.4%
497003
 
0.4%
678003
 
0.4%
467003
 
0.4%
596003
 
0.4%
440003
 
0.4%
Other values (260)324
46.3%
ValueCountFrequency (%)
0347
49.6%
8001
 
0.1%
100001
 
0.1%
110001
 
0.1%
121001
 
0.1%
128001
 
0.1%
131001
 
0.1%
141001
 
0.1%
161001
 
0.1%
176001
 
0.1%
ValueCountFrequency (%)
1005001
0.1%
919001
0.1%
907001
0.1%
888001
0.1%
884001
0.1%
849001
0.1%
836001
0.1%
832001
0.1%
826001
0.1%
824001
0.1%

capital-loss
Real number (ℝ)

Zeros 

Distinct276
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-26228
Minimum-111100
Maximum0
Zeros343
Zeros (%)49.0%
Negative357
Negative (%)51.0%
Memory size5.6 KiB
2026-02-26T15:59:27.851821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72520
Q1-51275
median-15650
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)51275

Descriptive statistics

Standard deviation28422.992
Coefficient of variation (CV)-1.0836889
Kurtosis-1.2737862
Mean-26228
Median Absolute Deviation (MAD)15650
Skewness-0.45453952
Sum-18359600
Variance8.0786645 × 108
MonotonicityNot monotonic
2026-02-26T15:59:27.970358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0343
49.0%
-538004
 
0.6%
-314004
 
0.6%
-614004
 
0.6%
-510004
 
0.6%
-492003
 
0.4%
-537003
 
0.4%
-490003
 
0.4%
-556003
 
0.4%
-500003
 
0.4%
Other values (266)326
46.6%
ValueCountFrequency (%)
-1111001
0.1%
-914001
0.1%
-912001
0.1%
-902001
0.1%
-901001
0.1%
-883001
0.1%
-873001
0.1%
-859001
0.1%
-839001
0.1%
-832001
0.1%
ValueCountFrequency (%)
0343
49.0%
-63001
 
0.1%
-85001
 
0.1%
-106001
 
0.1%
-121001
 
0.1%
-132001
 
0.1%
-138001
 
0.1%
-156001
 
0.1%
-157001
 
0.1%
-159001
 
0.1%
Distinct60
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
Minimum2015-01-01 00:00:00
Maximum2015-03-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-26T15:59:28.090234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:28.221575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

incident_type
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Multi-vehicle Collision
302 
Single Vehicle Collision
275 
Vehicle Theft
68 
Parked Car
55 

Length

Max length24
Median length23
Mean length21.4
Min length10

Characters and Unicode

Total characters14980
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle Vehicle Collision
2nd rowMulti-vehicle Collision
3rd rowSingle Vehicle Collision
4th rowMulti-vehicle Collision
5th rowMulti-vehicle Collision

Common Values

ValueCountFrequency (%)
Multi-vehicle Collision302
43.1%
Single Vehicle Collision275
39.3%
Vehicle Theft68
 
9.7%
Parked Car55
 
7.9%

Length

2026-02-26T15:59:28.335926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:28.407257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collision577
34.4%
vehicle343
20.5%
multi-vehicle302
18.0%
single275
16.4%
theft68
 
4.1%
parked55
 
3.3%
car55
 
3.3%

Most occurring characters

ValueCountFrequency (%)
l2376
15.9%
i2376
15.9%
e1688
11.3%
o1154
 
7.7%
975
 
6.5%
n852
 
5.7%
h713
 
4.8%
c645
 
4.3%
C632
 
4.2%
s577
 
3.9%
Other values (15)2992
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)14980
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l2376
15.9%
i2376
15.9%
e1688
11.3%
o1154
 
7.7%
975
 
6.5%
n852
 
5.7%
h713
 
4.8%
c645
 
4.3%
C632
 
4.2%
s577
 
3.9%
Other values (15)2992
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14980
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l2376
15.9%
i2376
15.9%
e1688
11.3%
o1154
 
7.7%
975
 
6.5%
n852
 
5.7%
h713
 
4.8%
c645
 
4.3%
C632
 
4.2%
s577
 
3.9%
Other values (15)2992
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14980
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l2376
15.9%
i2376
15.9%
e1688
11.3%
o1154
 
7.7%
975
 
6.5%
n852
 
5.7%
h713
 
4.8%
c645
 
4.3%
C632
 
4.2%
s577
 
3.9%
Other values (15)2992
20.0%

collision_type
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size47.3 KiB
Rear Collision
208 
Side Collision
194 
Front Collision
175 
?
123 

Length

Max length15
Median length14
Mean length11.965714
Min length1

Characters and Unicode

Total characters8376
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSide Collision
2nd rowSide Collision
3rd rowSide Collision
4th rowFront Collision
5th rowRear Collision

Common Values

ValueCountFrequency (%)
Rear Collision208
29.7%
Side Collision194
27.7%
Front Collision175
25.0%
?123
17.6%

Length

2026-02-26T15:59:28.497787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:28.564397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
collision577
45.2%
rear208
 
16.3%
side194
 
15.2%
front175
 
13.7%
123
 
9.6%

Most occurring characters

ValueCountFrequency (%)
i1348
16.1%
o1329
15.9%
l1154
13.8%
n752
9.0%
577
6.9%
s577
6.9%
C577
6.9%
e402
 
4.8%
r383
 
4.6%
a208
 
2.5%
Other values (6)1069
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i1348
16.1%
o1329
15.9%
l1154
13.8%
n752
9.0%
577
6.9%
s577
6.9%
C577
6.9%
e402
 
4.8%
r383
 
4.6%
a208
 
2.5%
Other values (6)1069
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i1348
16.1%
o1329
15.9%
l1154
13.8%
n752
9.0%
577
6.9%
s577
6.9%
C577
6.9%
e402
 
4.8%
r383
 
4.6%
a208
 
2.5%
Other values (6)1069
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i1348
16.1%
o1329
15.9%
l1154
13.8%
n752
9.0%
577
6.9%
s577
6.9%
C577
6.9%
e402
 
4.8%
r383
 
4.6%
a208
 
2.5%
Other values (6)1069
12.8%

incident_severity
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size47.0 KiB
Minor Damage
237 
Major Damage
200 
Total Loss
199 
Trivial Damage
64 

Length

Max length14
Median length12
Mean length11.614286
Min length10

Characters and Unicode

Total characters8130
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTotal Loss
2nd rowMinor Damage
3rd rowTotal Loss
4th rowMajor Damage
5th rowTotal Loss

Common Values

ValueCountFrequency (%)
Minor Damage237
33.9%
Major Damage200
28.6%
Total Loss199
28.4%
Trivial Damage64
 
9.1%

Length

2026-02-26T15:59:28.658829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:28.726513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
damage501
35.8%
minor237
16.9%
major200
 
14.3%
total199
 
14.2%
loss199
 
14.2%
trivial64
 
4.6%

Most occurring characters

ValueCountFrequency (%)
a1465
18.0%
o835
10.3%
700
 
8.6%
D501
 
6.2%
m501
 
6.2%
r501
 
6.2%
g501
 
6.2%
e501
 
6.2%
M437
 
5.4%
s398
 
4.9%
Other values (8)1790
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)8130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a1465
18.0%
o835
10.3%
700
 
8.6%
D501
 
6.2%
m501
 
6.2%
r501
 
6.2%
g501
 
6.2%
e501
 
6.2%
M437
 
5.4%
s398
 
4.9%
Other values (8)1790
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a1465
18.0%
o835
10.3%
700
 
8.6%
D501
 
6.2%
m501
 
6.2%
r501
 
6.2%
g501
 
6.2%
e501
 
6.2%
M437
 
5.4%
s398
 
4.9%
Other values (8)1790
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a1465
18.0%
o835
10.3%
700
 
8.6%
D501
 
6.2%
m501
 
6.2%
r501
 
6.2%
g501
 
6.2%
e501
 
6.2%
M437
 
5.4%
s398
 
4.9%
Other values (8)1790
22.0%

authorities_contacted
Categorical

Missing 

Distinct4
Distinct (%)0.6%
Missing60
Missing (%)8.6%
Memory size42.8 KiB
Police
199 
Fire
153 
Ambulance
145 
Other
143 

Length

Max length9
Median length6
Mean length5.978125
Min length4

Characters and Unicode

Total characters3826
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAmbulance
2nd rowOther
3rd rowPolice
4th rowFire
5th rowFire

Common Values

ValueCountFrequency (%)
Police199
28.4%
Fire153
21.9%
Ambulance145
20.7%
Other143
20.4%
(Missing)60
 
8.6%

Length

2026-02-26T15:59:28.947882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:29.015502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
police199
31.1%
fire153
23.9%
ambulance145
22.7%
other143
22.3%

Most occurring characters

ValueCountFrequency (%)
e640
16.7%
i352
 
9.2%
c344
 
9.0%
l344
 
9.0%
r296
 
7.7%
P199
 
5.2%
o199
 
5.2%
F153
 
4.0%
A145
 
3.8%
m145
 
3.8%
Other values (7)1009
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)3826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e640
16.7%
i352
 
9.2%
c344
 
9.0%
l344
 
9.0%
r296
 
7.7%
P199
 
5.2%
o199
 
5.2%
F153
 
4.0%
A145
 
3.8%
m145
 
3.8%
Other values (7)1009
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e640
16.7%
i352
 
9.2%
c344
 
9.0%
l344
 
9.0%
r296
 
7.7%
P199
 
5.2%
o199
 
5.2%
F153
 
4.0%
A145
 
3.8%
m145
 
3.8%
Other values (7)1009
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e640
16.7%
i352
 
9.2%
c344
 
9.0%
l344
 
9.0%
r296
 
7.7%
P199
 
5.2%
o199
 
5.2%
F153
 
4.0%
A145
 
3.8%
m145
 
3.8%
Other values (7)1009
26.4%

incident_state
Categorical

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size40.5 KiB
NY
187 
SC
178 
WV
149 
NC
78 
VA
69 
Other values (2)
39 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1400
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowNC
3rd rowWV
4th rowWV
5th rowSC

Common Values

ValueCountFrequency (%)
NY187
26.7%
SC178
25.4%
WV149
21.3%
NC78
11.1%
VA69
 
9.9%
PA21
 
3.0%
OH18
 
2.6%

Length

2026-02-26T15:59:29.112741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:29.197240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ny187
26.7%
sc178
25.4%
wv149
21.3%
nc78
11.1%
va69
 
9.9%
pa21
 
3.0%
oh18
 
2.6%

Most occurring characters

ValueCountFrequency (%)
N265
18.9%
C256
18.3%
V218
15.6%
Y187
13.4%
S178
12.7%
W149
10.6%
A90
 
6.4%
P21
 
1.5%
O18
 
1.3%
H18
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N265
18.9%
C256
18.3%
V218
15.6%
Y187
13.4%
S178
12.7%
W149
10.6%
A90
 
6.4%
P21
 
1.5%
O18
 
1.3%
H18
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N265
18.9%
C256
18.3%
V218
15.6%
Y187
13.4%
S178
12.7%
W149
10.6%
A90
 
6.4%
P21
 
1.5%
O18
 
1.3%
H18
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N265
18.9%
C256
18.3%
V218
15.6%
Y187
13.4%
S178
12.7%
W149
10.6%
A90
 
6.4%
P21
 
1.5%
O18
 
1.3%
H18
 
1.3%

incident_city
Categorical

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size45.4 KiB
Springfield
117 
Arlington
110 
Columbus
108 
Hillsdale
96 
Northbend
96 
Other values (2)
173 

Length

Max length11
Median length9
Mean length9.2985714
Min length8

Characters and Unicode

Total characters6509
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRiverwood
2nd rowSpringfield
3rd rowNorthbend
4th rowNorthbend
5th rowNorthbend

Common Values

ValueCountFrequency (%)
Springfield117
16.7%
Arlington110
15.7%
Columbus108
15.4%
Hillsdale96
13.7%
Northbend96
13.7%
Riverwood90
12.9%
Northbrook83
11.9%

Length

2026-02-26T15:59:29.297684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:29.374100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
springfield117
16.7%
arlington110
15.7%
columbus108
15.4%
hillsdale96
13.7%
northbend96
13.7%
riverwood90
12.9%
northbrook83
11.9%

Most occurring characters

ValueCountFrequency (%)
o743
 
11.4%
l623
 
9.6%
r579
 
8.9%
i530
 
8.1%
n433
 
6.7%
d399
 
6.1%
e399
 
6.1%
t289
 
4.4%
b287
 
4.4%
g227
 
3.5%
Other values (16)2000
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)6509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o743
 
11.4%
l623
 
9.6%
r579
 
8.9%
i530
 
8.1%
n433
 
6.7%
d399
 
6.1%
e399
 
6.1%
t289
 
4.4%
b287
 
4.4%
g227
 
3.5%
Other values (16)2000
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o743
 
11.4%
l623
 
9.6%
r579
 
8.9%
i530
 
8.1%
n433
 
6.7%
d399
 
6.1%
e399
 
6.1%
t289
 
4.4%
b287
 
4.4%
g227
 
3.5%
Other values (16)2000
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o743
 
11.4%
l623
 
9.6%
r579
 
8.9%
i530
 
8.1%
n433
 
6.7%
d399
 
6.1%
e399
 
6.1%
t289
 
4.4%
b287
 
4.4%
g227
 
3.5%
Other values (16)2000
30.7%

incident_location
Text

Unique 

Distinct700
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size49.2 KiB
2026-02-26T15:59:29.546965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length23
Median length20
Mean length14.811429
Min length11

Characters and Unicode

Total characters10368
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique700 ?
Unique (%)100.0%

Sample

1st row7082 Oak Ridge
2nd row9352 Washington Ave
3rd row5061 Francis Ave
4th row2457 Washington Ave
5th row2290 4th Ave
ValueCountFrequency (%)
lane130
 
6.2%
ridge121
 
5.8%
drive121
 
5.8%
ave113
 
5.4%
st113
 
5.4%
hwy102
 
4.9%
4th39
 
1.9%
5th38
 
1.8%
texas36
 
1.7%
weaver34
 
1.6%
Other values (695)1253
59.7%
2026-02-26T15:59:29.786388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1400
 
13.5%
e900
 
8.7%
i435
 
4.2%
a432
 
4.2%
n367
 
3.5%
r348
 
3.4%
t324
 
3.1%
5322
 
3.1%
2310
 
3.0%
1308
 
3.0%
Other values (39)5222
50.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1400
 
13.5%
e900
 
8.7%
i435
 
4.2%
a432
 
4.2%
n367
 
3.5%
r348
 
3.4%
t324
 
3.1%
5322
 
3.1%
2310
 
3.0%
1308
 
3.0%
Other values (39)5222
50.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1400
 
13.5%
e900
 
8.7%
i435
 
4.2%
a432
 
4.2%
n367
 
3.5%
r348
 
3.4%
t324
 
3.1%
5322
 
3.1%
2310
 
3.0%
1308
 
3.0%
Other values (39)5222
50.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1400
 
13.5%
e900
 
8.7%
i435
 
4.2%
a432
 
4.2%
n367
 
3.5%
r348
 
3.4%
t324
 
3.1%
5322
 
3.1%
2310
 
3.0%
1308
 
3.0%
Other values (39)5222
50.4%

incident_hour_of_the_day
Real number (ℝ)

Zeros 

Distinct24
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.642857
Minimum0
Maximum23
Zeros35
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:29.860047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q15
median12
Q317
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9645243
Coefficient of variation (CV)0.59818
Kurtosis-1.2031482
Mean11.642857
Median Absolute Deviation (MAD)6
Skewness-0.044567362
Sum8150
Variance48.504598
MonotonicityNot monotonic
2026-02-26T15:59:29.949934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1738
 
5.4%
2336
 
5.1%
336
 
5.1%
435
 
5.0%
1635
 
5.0%
035
 
5.0%
1334
 
4.9%
1032
 
4.6%
1431
 
4.4%
2131
 
4.4%
Other values (14)357
51.0%
ValueCountFrequency (%)
035
5.0%
121
3.0%
225
3.6%
336
5.1%
435
5.0%
525
3.6%
625
3.6%
727
3.9%
827
3.9%
926
3.7%
ValueCountFrequency (%)
2336
5.1%
2224
3.4%
2131
4.4%
2024
3.4%
1929
4.1%
1828
4.0%
1738
5.4%
1635
5.0%
1527
3.9%
1431
4.4%

number_of_vehicles_involved
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
1
398 
3
251 
4
 
28
2
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row1
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1398
56.9%
3251
35.9%
428
 
4.0%
223
 
3.3%

Length

2026-02-26T15:59:30.047792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:30.113632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1398
56.9%
3251
35.9%
428
 
4.0%
223
 
3.3%

Most occurring characters

ValueCountFrequency (%)
1398
56.9%
3251
35.9%
428
 
4.0%
223
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1398
56.9%
3251
35.9%
428
 
4.0%
223
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1398
56.9%
3251
35.9%
428
 
4.0%
223
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1398
56.9%
3251
35.9%
428
 
4.0%
223
 
3.3%

property_damage
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size40.4 KiB
?
259 
NO
223 
YES
218 

Length

Max length3
Median length2
Mean length1.9414286
Min length1

Characters and Unicode

Total characters1359
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd row?
3rd row?
4th rowYES
5th rowYES

Common Values

ValueCountFrequency (%)
?259
37.0%
NO223
31.9%
YES218
31.1%

Length

2026-02-26T15:59:30.200047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:30.265334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
259
37.0%
no223
31.9%
yes218
31.1%

Most occurring characters

ValueCountFrequency (%)
?259
19.1%
N223
16.4%
O223
16.4%
Y218
16.0%
E218
16.0%
S218
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1359
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
?259
19.1%
N223
16.4%
O223
16.4%
Y218
16.0%
E218
16.0%
S218
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1359
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
?259
19.1%
N223
16.4%
O223
16.4%
Y218
16.0%
E218
16.0%
S218
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1359
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
?259
19.1%
N223
16.4%
O223
16.4%
Y218
16.0%
E218
16.0%
S218
16.0%

bodily_injuries
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
0
236 
2
232 
1
232 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0236
33.7%
2232
33.1%
1232
33.1%

Length

2026-02-26T15:59:30.347757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:30.412061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0236
33.7%
2232
33.1%
1232
33.1%

Most occurring characters

ValueCountFrequency (%)
0236
33.7%
2232
33.1%
1232
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0236
33.7%
2232
33.1%
1232
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0236
33.7%
2232
33.1%
1232
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0236
33.7%
2232
33.1%
1232
33.1%

witnesses
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
1
181 
0
177 
2
173 
3
169 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1181
25.9%
0177
25.3%
2173
24.7%
3169
24.1%

Length

2026-02-26T15:59:30.491112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:30.557564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1181
25.9%
0177
25.3%
2173
24.7%
3169
24.1%

Most occurring characters

ValueCountFrequency (%)
1181
25.9%
0177
25.3%
2173
24.7%
3169
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1181
25.9%
0177
25.3%
2173
24.7%
3169
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1181
25.9%
0177
25.3%
2173
24.7%
3169
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1181
25.9%
0177
25.3%
2173
24.7%
3169
24.1%
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size40.4 KiB
?
247 
NO
236 
YES
217 

Length

Max length3
Median length2
Mean length1.9571429
Min length1

Characters and Unicode

Total characters1370
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row?
2nd rowYES
3rd rowNO
4th rowYES
5th rowYES

Common Values

ValueCountFrequency (%)
?247
35.3%
NO236
33.7%
YES217
31.0%

Length

2026-02-26T15:59:30.652576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:30.714492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
247
35.3%
no236
33.7%
yes217
31.0%

Most occurring characters

ValueCountFrequency (%)
?247
18.0%
N236
17.2%
O236
17.2%
Y217
15.8%
E217
15.8%
S217
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
?247
18.0%
N236
17.2%
O236
17.2%
Y217
15.8%
E217
15.8%
S217
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
?247
18.0%
N236
17.2%
O236
17.2%
Y217
15.8%
E217
15.8%
S217
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
?247
18.0%
N236
17.2%
O236
17.2%
Y217
15.8%
E217
15.8%
S217
15.8%

total_claim_amount
Real number (ℝ)

High correlation 

Distinct573
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52475.686
Minimum100
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:30.807130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4396
Q141662.5
median57710
Q370072.5
95-th percentile86805.5
Maximum114920
Range114820
Interquartile range (IQR)28410

Descriptive statistics

Standard deviation26115.22
Coefficient of variation (CV)0.49766324
Kurtosis-0.44172288
Mean52475.686
Median Absolute Deviation (MAD)13760
Skewness-0.60164742
Sum36732980
Variance6.820047 × 108
MonotonicityNot monotonic
2026-02-26T15:59:30.924276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26404
 
0.6%
754004
 
0.6%
531003
 
0.4%
606003
 
0.4%
774403
 
0.4%
614903
 
0.4%
585003
 
0.4%
693003
 
0.4%
845903
 
0.4%
583003
 
0.4%
Other values (563)668
95.4%
ValueCountFrequency (%)
1001
 
0.1%
19201
 
0.1%
21601
 
0.1%
22501
 
0.1%
26404
0.6%
27002
0.3%
28001
 
0.1%
28601
 
0.1%
29701
 
0.1%
30802
0.3%
ValueCountFrequency (%)
1149201
0.1%
1084801
0.1%
1080301
0.1%
1079001
0.1%
1058201
0.1%
1050401
0.1%
1035601
0.1%
999901
0.1%
993201
0.1%
982801
0.1%

injury_claim
Real number (ℝ)

High correlation  Zeros 

Distinct504
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7463.2571
Minimum0
Maximum21450
Zeros19
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:31.043465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14265
median6820
Q311235
95-th percentile15784
Maximum21450
Range21450
Interquartile range (IQR)6970

Descriptive statistics

Standard deviation4890.6091
Coefficient of variation (CV)0.65529152
Kurtosis-0.74232691
Mean7463.2571
Median Absolute Deviation (MAD)3720
Skewness0.25317287
Sum5224280
Variance23918058
MonotonicityNot monotonic
2026-02-26T15:59:31.157938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
2.7%
4807
 
1.0%
7805
 
0.7%
6405
 
0.7%
55404
 
0.6%
9004
 
0.6%
6804
 
0.6%
8604
 
0.6%
57103
 
0.4%
6303
 
0.4%
Other values (494)642
91.7%
ValueCountFrequency (%)
019
2.7%
101
 
0.1%
2201
 
0.1%
2501
 
0.1%
2801
 
0.1%
2901
 
0.1%
3002
 
0.3%
3302
 
0.3%
4001
 
0.1%
4203
 
0.4%
ValueCountFrequency (%)
214501
0.1%
213301
0.1%
207001
0.1%
181801
0.1%
180801
0.1%
178801
0.1%
176801
0.1%
175801
0.1%
174601
0.1%
174001
0.1%

property_claim
Real number (ℝ)

High correlation  Zeros 

Distinct506
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7324.1429
Minimum0
Maximum23670
Zeros14
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:31.271757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14420
median6685
Q310825
95-th percentile15444
Maximum23670
Range23670
Interquartile range (IQR)6405

Descriptive statistics

Standard deviation4778.111
Coefficient of variation (CV)0.65237818
Kurtosis-0.32470775
Mean7324.1429
Median Absolute Deviation (MAD)3185
Skewness0.39928338
Sum5126900
Variance22830345
MonotonicityNot monotonic
2026-02-26T15:59:31.508989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014
 
2.0%
6604
 
0.6%
66204
 
0.6%
8604
 
0.6%
6804
 
0.6%
6404
 
0.6%
8404
 
0.6%
110804
 
0.6%
4804
 
0.6%
53403
 
0.4%
Other values (496)651
93.0%
ValueCountFrequency (%)
014
2.0%
201
 
0.1%
2401
 
0.1%
2501
 
0.1%
2601
 
0.1%
2802
 
0.3%
3002
 
0.3%
3202
 
0.3%
3301
 
0.1%
3902
 
0.3%
ValueCountFrequency (%)
236701
0.1%
216301
0.1%
215801
0.1%
212401
0.1%
199501
0.1%
196501
0.1%
194701
0.1%
192601
0.1%
192001
0.1%
181801
0.1%

vehicle_claim
Real number (ℝ)

High correlation 

Distinct561
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37688.286
Minimum70
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:31.638423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile3289.5
Q129872.5
median41760
Q350417.5
95-th percentile61777
Maximum79560
Range79490
Interquartile range (IQR)20545

Descriptive statistics

Standard deviation18695.687
Coefficient of variation (CV)0.49606094
Kurtosis-0.4242117
Mean37688.286
Median Absolute Deviation (MAD)9700
Skewness-0.61171744
Sum26381800
Variance3.495287 × 108
MonotonicityNot monotonic
2026-02-26T15:59:31.767820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50406
 
0.9%
448005
 
0.7%
36005
 
0.7%
336005
 
0.7%
33604
 
0.6%
453603
 
0.4%
46203
 
0.4%
427203
 
0.4%
47203
 
0.4%
461603
 
0.4%
Other values (551)660
94.3%
ValueCountFrequency (%)
701
0.1%
14402
0.3%
16802
0.3%
17501
0.1%
17601
0.1%
19601
0.1%
19801
0.1%
20301
0.1%
20801
0.1%
21002
0.3%
ValueCountFrequency (%)
795601
0.1%
776701
0.1%
764001
0.1%
756001
0.1%
755301
0.1%
747901
0.1%
736201
0.1%
732601
0.1%
727201
0.1%
723202
0.3%

auto_make
Categorical

High correlation 

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size43.0 KiB
Suburu
57 
Nissan
56 
Saab
56 
Toyota
55 
Chevrolet
54 
Other values (9)
422 

Length

Max length10
Median length9
Mean length5.7671429
Min length3

Characters and Unicode

Total characters4037
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNissan
2nd rowHonda
3rd rowJeep
4th rowSuburu
5th rowFord

Common Values

ValueCountFrequency (%)
Suburu57
 
8.1%
Nissan56
 
8.0%
Saab56
 
8.0%
Toyota55
 
7.9%
Chevrolet54
 
7.7%
Volkswagen54
 
7.7%
Audi51
 
7.3%
Dodge49
 
7.0%
Ford49
 
7.0%
BMW48
 
6.9%
Other values (4)171
24.4%

Length

2026-02-26T15:59:31.881307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suburu57
 
8.1%
nissan56
 
8.0%
saab56
 
8.0%
toyota55
 
7.9%
chevrolet54
 
7.7%
volkswagen54
 
7.7%
audi51
 
7.3%
dodge49
 
7.0%
ford49
 
7.0%
bmw48
 
6.9%
Other values (4)171
24.4%

Most occurring characters

ValueCountFrequency (%)
e439
 
10.9%
a357
 
8.8%
o354
 
8.8%
u264
 
6.5%
r248
 
6.1%
d233
 
5.8%
s212
 
5.3%
n148
 
3.7%
c130
 
3.2%
S113
 
2.8%
Other values (23)1539
38.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4037
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e439
 
10.9%
a357
 
8.8%
o354
 
8.8%
u264
 
6.5%
r248
 
6.1%
d233
 
5.8%
s212
 
5.3%
n148
 
3.7%
c130
 
3.2%
S113
 
2.8%
Other values (23)1539
38.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4037
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e439
 
10.9%
a357
 
8.8%
o354
 
8.8%
u264
 
6.5%
r248
 
6.1%
d233
 
5.8%
s212
 
5.3%
n148
 
3.7%
c130
 
3.2%
S113
 
2.8%
Other values (23)1539
38.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4037
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e439
 
10.9%
a357
 
8.8%
o354
 
8.8%
u264
 
6.5%
r248
 
6.1%
d233
 
5.8%
s212
 
5.3%
n148
 
3.7%
c130
 
3.2%
S113
 
2.8%
Other values (23)1539
38.1%

auto_model
Categorical

High correlation 

Distinct39
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size42.7 KiB
Wrangler
 
31
RAM
 
29
A3
 
28
Passat
 
28
Jetta
 
26
Other values (34)
558 

Length

Max length14
Median length9
Mean length5.2385714
Min length2

Characters and Unicode

Total characters3667
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMaxima
2nd rowCivic
3rd rowWrangler
4th rowLegacy
5th rowF150

Common Values

ValueCountFrequency (%)
Wrangler31
 
4.4%
RAM29
 
4.1%
A328
 
4.0%
Passat28
 
4.0%
Jetta26
 
3.7%
Legacy23
 
3.3%
A523
 
3.3%
Pathfinder23
 
3.3%
MDX23
 
3.3%
E40022
 
3.1%
Other values (29)444
63.4%

Length

2026-02-26T15:59:31.980005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wrangler31
 
4.3%
ram29
 
4.0%
a328
 
3.9%
passat28
 
3.9%
jetta26
 
3.6%
legacy23
 
3.2%
a523
 
3.2%
pathfinder23
 
3.2%
mdx23
 
3.2%
e40022
 
3.0%
Other values (31)471
64.8%

Most occurring characters

ValueCountFrequency (%)
a362
 
9.9%
e296
 
8.1%
r284
 
7.7%
i170
 
4.6%
o164
 
4.5%
t141
 
3.8%
l137
 
3.7%
s122
 
3.3%
n121
 
3.3%
M113
 
3.1%
Other values (42)1757
47.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)3667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a362
 
9.9%
e296
 
8.1%
r284
 
7.7%
i170
 
4.6%
o164
 
4.5%
t141
 
3.8%
l137
 
3.7%
s122
 
3.3%
n121
 
3.3%
M113
 
3.1%
Other values (42)1757
47.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a362
 
9.9%
e296
 
8.1%
r284
 
7.7%
i170
 
4.6%
o164
 
4.5%
t141
 
3.8%
l137
 
3.7%
s122
 
3.3%
n121
 
3.3%
M113
 
3.1%
Other values (42)1757
47.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a362
 
9.9%
e296
 
8.1%
r284
 
7.7%
i170
 
4.6%
o164
 
4.5%
t141
 
3.8%
l137
 
3.7%
s122
 
3.3%
n121
 
3.3%
M113
 
3.1%
Other values (42)1757
47.9%

auto_year
Real number (ℝ)

Distinct21
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.9314
Minimum1995
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2026-02-26T15:59:32.058016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q12000
median2005
Q32010
95-th percentile2014
Maximum2015
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9833709
Coefficient of variation (CV)0.002984327
Kurtosis-1.1607134
Mean2004.9314
Median Absolute Deviation (MAD)5
Skewness-0.0057857289
Sum1403452
Variance35.800728
MonotonicityNot monotonic
2026-02-26T15:59:32.147987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
199542
 
6.0%
200241
 
5.9%
200940
 
5.7%
200638
 
5.4%
200538
 
5.4%
201336
 
5.1%
199936
 
5.1%
200336
 
5.1%
200035
 
5.0%
201134
 
4.9%
Other values (11)324
46.3%
ValueCountFrequency (%)
199542
6.0%
199625
3.6%
199729
4.1%
199831
4.4%
199936
5.1%
200035
5.0%
200129
4.1%
200241
5.9%
200336
5.1%
200428
4.0%
ValueCountFrequency (%)
201531
4.4%
201427
3.9%
201336
5.1%
201232
4.6%
201134
4.9%
201033
4.7%
200940
5.7%
200826
3.7%
200733
4.7%
200638
5.4%

fraud_reported
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size39.8 KiB
0
519 
1
181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters700
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0519
74.1%
1181
 
25.9%

Length

2026-02-26T15:59:32.254171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-26T15:59:32.314536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0519
74.1%
1181
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0519
74.1%
1181
 
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0519
74.1%
1181
 
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0519
74.1%
1181
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0519
74.1%
1181
 
25.9%

_c39
Unsupported

Missing  Rejected  Unsupported 

Missing700
Missing (%)100.0%
Memory size5.6 KiB

Interactions

2026-02-26T15:59:22.651185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:05.721989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:08.220946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:09.594443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:10.938598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:12.256026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.577629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.805620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.154733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.399209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.794560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.968259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.142051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.746157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:05.812744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:13.660457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.004410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.242828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:21.240489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:09.781448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:12.439142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:15.084739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:18.953280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.126188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.351649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.928955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:08.462426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:09.873251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.184443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:12.527778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.817516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.170993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.408567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.669768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.034036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.204555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.458505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:08.558186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:09.968274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.276379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:12.628580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.913097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.264142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.517210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.765941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.125230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.296128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.698253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:23.130445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:08.652596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:10.060631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.356185image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:12.722354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.002420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.349542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:17.860837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:21.793314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:06.333386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:07.538759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:08.773455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:12.817604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.097283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.445462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.707021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.959092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.301047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.469366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.896681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:23.330465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:06.422154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:07.620939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:08.861874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:11.543499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:12.912238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.179698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.530589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.793661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.051573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.381626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.550924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.988310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:23.426685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:06.518362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:07.704853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:08.947994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:10.361312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.626896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.005400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.267546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.616129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.877432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.143045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.464372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.637512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.080755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:23.516668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:06.600294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:10.455031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.709863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.096403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.355920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.698186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.957932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.341849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.542965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.716834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:10.551908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.796218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.193731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.442745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.788526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.052147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.431854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.629042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.802076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.269487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:23.706634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:11.878692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.281824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.530768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:15.867981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.134141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.517722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.704957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.879593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.357773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:10.732247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:11.963180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-26T15:59:18.600861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.785856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:20.955249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.446168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:23.902289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:06.944704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:08.126060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:09.489382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:10.835148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:12.164514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:13.474584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:14.710468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:16.045860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:17.307222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:18.697005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:19.875161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:21.049456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-26T15:59:22.547223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-26T15:59:32.413275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ageauthorities_contactedauto_makeauto_modelauto_yearbodily_injuriescapital-gainscapital-losscollision_typefraud_reportedincident_cityincident_hour_of_the_dayincident_severityincident_stateincident_typeinjury_claiminsured_education_levelinsured_hobbiesinsured_occupationinsured_relationshipinsured_sexinsured_zipmonths_as_customernumber_of_vehicles_involvedpolice_report_availablepolicy_annual_premiumpolicy_cslpolicy_deductablepolicy_numberpolicy_stateproperty_claimproperty_damagetotal_claim_amountumbrella_limitvehicle_claimwitnesses
age1.0000.0000.0000.000-0.0160.056-0.0500.0040.0710.0720.0000.1140.0740.0000.0000.0540.0000.0000.0000.0480.015-0.0110.9120.1130.0000.0630.0240.0430.0370.0000.0390.0000.053-0.0020.0430.016
authorities_contacted0.0001.0000.0860.0380.0770.0000.0000.0000.2800.0900.0380.1200.2160.0000.2780.2810.0000.0000.0000.0110.0690.0000.0000.0500.0470.0320.0520.0310.0000.0000.2640.0000.2910.0000.2760.000
auto_make0.0000.0861.0000.9820.0000.0000.0000.0000.0000.0000.0280.0000.0000.0000.0000.0120.0790.0410.0000.0000.0000.0380.0000.0880.0000.0260.0000.0480.0000.0450.0000.0000.0410.0000.0000.000
auto_model0.0000.0380.9821.0000.0300.0000.0360.0000.0000.0740.0560.0000.0000.0380.1110.0000.0230.0110.0000.0000.0000.0390.0000.1350.0270.0000.0000.0000.0000.0000.0490.0000.0370.0000.0000.000
auto_year-0.0160.0770.0000.0301.0000.0610.025-0.0670.0310.0370.0000.0400.0440.0580.062-0.0150.0450.0000.0560.0460.020-0.036-0.0320.0640.000-0.0020.0000.040-0.0110.0000.0200.000-0.023-0.006-0.0330.000
bodily_injuries0.0560.0000.0000.0000.0611.0000.0900.0000.0000.0000.0310.0000.0000.0000.0290.0320.0000.0170.1270.0000.0250.0000.0540.0000.0320.0000.0380.0300.0000.0190.0000.0000.0260.0490.0270.000
capital-gains-0.0500.0000.0000.0360.0250.0901.000-0.0100.0000.0000.000-0.0610.0170.0000.0660.0450.0360.0680.0000.0000.0780.044-0.0370.1080.000-0.0260.0000.000-0.0160.0510.0210.0430.031-0.0440.0230.060
capital-loss0.0040.0000.0000.000-0.0670.000-0.0101.0000.0510.0000.050-0.0500.0000.0000.000-0.0560.0610.0000.0000.0870.0460.0650.0240.0000.0600.0120.0000.000-0.0110.000-0.0380.000-0.053-0.041-0.0480.000
collision_type0.0710.2800.0000.0000.0310.0000.0000.0511.0000.1720.0340.2760.4290.0860.5760.5300.0470.0370.0480.0360.0000.0000.0000.2290.0250.0000.0770.0000.0520.0000.5420.0000.5780.0000.5770.052
fraud_reported0.0720.0900.0000.0740.0370.0000.0000.0000.1721.0000.0000.0750.4920.1160.1590.1310.0000.3770.0000.0000.0000.0670.0000.0490.0000.0000.0000.0490.0000.0000.1600.0520.1480.0000.1550.097
incident_city0.0000.0380.0280.0560.0000.0310.0000.0500.0340.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0490.0000.0350.0000.1020.0000.000
incident_hour_of_the_day0.1140.1200.0000.0000.0400.000-0.061-0.0500.2760.0750.0001.0000.2010.0000.2700.1800.0190.0000.0000.0420.0000.0050.1030.1130.0000.0230.0000.0880.0110.0000.1640.0400.176-0.0120.1700.050
incident_severity0.0740.2160.0000.0000.0440.0000.0170.0000.4290.4920.0000.2011.0000.0440.4320.3960.0000.0320.0000.0310.0000.0510.0690.1760.0360.0000.0000.0000.0000.0000.3930.0680.4290.0920.4310.000
incident_state0.0000.0000.0000.0380.0580.0000.0000.0000.0860.1160.0000.0000.0441.0000.0410.0000.0000.0840.0000.0540.0540.0660.0000.0440.0650.0410.0000.0000.0000.0000.0450.0400.0200.0540.0280.000
incident_type0.0000.2780.0000.1110.0620.0290.0660.0000.5760.1590.0000.2700.4320.0411.0000.5260.0640.0390.0660.0000.0890.0000.0000.5750.0000.0110.0000.0000.0500.0000.5340.0000.5720.0000.5730.000
injury_claim0.0540.2810.0120.000-0.0150.0320.045-0.0560.5300.1310.0000.1800.3960.0000.5261.0000.0000.0290.0640.0320.0000.0090.0490.2010.000-0.0040.0480.073-0.0310.0830.5690.0000.787-0.0600.6730.011
insured_education_level0.0000.0000.0790.0230.0450.0000.0360.0610.0470.0000.0000.0190.0000.0000.0640.0001.0000.0000.0290.0580.0190.0000.0000.0000.0350.0000.1120.0000.0620.0620.0000.0530.0520.0000.0550.052
insured_hobbies0.0000.0000.0410.0110.0000.0170.0680.0000.0370.3770.0000.0000.0320.0840.0390.0290.0001.0000.0510.0000.0510.0000.0280.0000.1170.0000.0000.0000.0590.0000.0000.0000.0580.0000.0340.068
insured_occupation0.0000.0000.0000.0000.0560.1270.0000.0000.0480.0000.0000.0000.0000.0000.0660.0640.0290.0511.0000.0000.0000.1020.0000.0000.0340.0310.0690.1010.0000.0000.0000.0000.0510.0330.0670.068
insured_relationship0.0480.0110.0000.0000.0460.0000.0000.0870.0360.0000.0000.0420.0310.0540.0000.0320.0580.0000.0001.0000.0000.0000.0690.0000.0000.0000.0520.0460.0460.0000.0000.0000.0200.0000.0000.061
insured_sex0.0150.0690.0000.0000.0200.0250.0780.0460.0000.0000.0000.0000.0000.0540.0890.0000.0190.0510.0000.0001.0000.0000.0880.0000.0360.1310.0580.0000.0000.0000.0000.0000.0510.0510.0970.000
insured_zip-0.0110.0000.0380.039-0.0360.0000.0440.0650.0000.0670.0000.0050.0510.0660.0000.0090.0000.0000.1020.0000.0001.0000.0030.0560.0980.0420.0000.000-0.0220.045-0.0170.0670.015-0.0280.0050.000
months_as_customer0.9120.0000.0000.000-0.0320.054-0.0370.0240.0000.0000.0210.1030.0690.0000.0000.0490.0000.0280.0000.0690.0880.0031.0000.0000.0600.0530.0000.0000.0410.0300.0260.0000.0560.0040.0550.000
number_of_vehicles_involved0.1130.0500.0880.1350.0640.0000.1080.0000.2290.0490.0000.1130.1760.0440.5750.2010.0000.0000.0000.0000.0000.0560.0001.0000.0000.0000.0000.0380.0300.0000.2070.0420.2350.0000.2360.012
police_report_available0.0000.0470.0000.0270.0000.0320.0000.0600.0250.0000.0000.0000.0360.0650.0000.0000.0350.1170.0340.0000.0360.0980.0600.0001.0000.0620.0530.0000.0000.0680.0000.0000.0410.0880.0510.000
policy_annual_premium0.0630.0320.0260.000-0.0020.000-0.0260.0120.0000.0000.0000.0230.0000.0410.011-0.0040.0000.0000.0310.0000.1310.0420.0530.0000.0621.0000.0880.0330.0100.0000.0350.0770.0310.0270.0440.000
policy_csl0.0240.0520.0000.0000.0000.0380.0000.0000.0770.0000.0000.0000.0000.0000.0000.0480.1120.0000.0690.0520.0580.0000.0000.0000.0530.0881.0000.0000.0750.0000.0000.0000.0000.0000.0000.052
policy_deductable0.0430.0310.0480.0000.0400.0300.0000.0000.0000.0490.0000.0880.0000.0000.0000.0730.0000.0000.1010.0460.0000.0000.0000.0380.0000.0330.0001.0000.0000.0470.0560.0000.1160.0000.0360.000
policy_number0.0370.0000.0000.000-0.0110.000-0.016-0.0110.0520.0000.0000.0110.0000.0000.050-0.0310.0620.0590.0000.0460.000-0.0220.0410.0300.0000.0100.0750.0001.0000.0000.0070.027-0.0150.004-0.0210.000
policy_state0.0000.0000.0450.0000.0000.0190.0510.0000.0000.0000.0490.0000.0000.0000.0000.0830.0620.0000.0000.0000.0000.0450.0300.0000.0680.0000.0000.0470.0001.0000.0000.0000.0000.0340.0000.045
property_claim0.0390.2640.0000.0490.0200.0000.021-0.0380.5420.1600.0000.1640.3930.0450.5340.5690.0000.0000.0000.0000.000-0.0170.0260.2070.0000.0350.0000.0560.0070.0001.0000.0560.793-0.0200.6800.000
property_damage0.0000.0000.0000.0000.0000.0000.0430.0000.0000.0520.0350.0400.0680.0400.0000.0000.0530.0000.0000.0000.0000.0670.0000.0420.0000.0770.0000.0000.0270.0000.0561.0000.0000.0620.0000.000
total_claim_amount0.0530.2910.0410.037-0.0230.0260.031-0.0530.5780.1480.0000.1760.4290.0200.5720.7870.0520.0580.0510.0200.0510.0150.0560.2350.0410.0310.0000.116-0.0150.0000.7930.0001.000-0.0530.9630.000
umbrella_limit-0.0020.0000.0000.000-0.0060.049-0.044-0.0410.0000.0000.102-0.0120.0920.0540.000-0.0600.0000.0000.0330.0000.051-0.0280.0040.0000.0880.0270.0000.0000.0040.034-0.0200.062-0.0531.000-0.0490.029
vehicle_claim0.0430.2760.0000.000-0.0330.0270.023-0.0480.5770.1550.0000.1700.4310.0280.5730.6730.0550.0340.0670.0000.0970.0050.0550.2360.0510.0440.0000.036-0.0210.0000.6800.0000.963-0.0491.0000.014
witnesses0.0160.0000.0000.0000.0000.0000.0600.0000.0520.0970.0000.0500.0000.0000.0000.0110.0520.0680.0680.0610.0000.0000.0000.0120.0000.0000.0520.0000.0000.0450.0000.0000.0000.0290.0141.000

Missing values

2026-02-26T15:59:24.233897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-26T15:59:24.518864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
0187371255912013-08-08IN500/100010001412.065000000450947FEMALEMastersprotective-servreadingnot-in-family6010002015-01-16Single Vehicle CollisionSide CollisionTotal LossAmbulanceNCRiverwood7082 Oak Ridge211?03?577005770577046160NissanMaxima20000NaN
1243449677131997-12-25IL250/500500809.110600208MALEJDcraft-repairpoloother-relative3320002015-01-27Multi-vehicle CollisionSide CollisionMinor DamageOtherNCSpringfield9352 Washington Ave43?21YES5140051401028035980HondaCivic19960NaN
224336490821996-01-19IL500/100010001922.840431277FEMALEHigh Schoolmachine-op-inspctskydivingwife0-452002015-01-24Single Vehicle CollisionSide CollisionTotal LossPoliceWVNorthbend5061 Francis Ave01?21NO468004680936032760JeepWrangler20020NaN
3215425193122008-10-28OH500/10005001848.810435489MALEJDtransport-movingvideo-gamesown-child0-490002015-02-06Multi-vehicle CollisionFront CollisionMajor DamageFireWVNorthbend2457 Washington Ave203YES22YES6852011420571051390SuburuLegacy20031NaN
485301905882001-12-09OH100/3001000796.350614166FEMALEMDcraft-repairvideo-gamesown-child72400-770002015-02-20Multi-vehicle CollisionRear CollisionTotal LossFireSCNorthbend2290 4th Ave93YES21YES5896053601072042880FordF15020040NaN
5310486701421999-08-06IN100/3005001516.340474167FEMALEJDadm-clericalsleepingunmarried1100002015-01-04Multi-vehicle CollisionRear CollisionMajor DamagePoliceSCSpringfield2823 Weaver Lane114YES02NO5940013200660039600Saab9319961NaN
6297487516122009-06-22IN250/50010001464.733000000443861MALEPhDexec-managerialgolfother-relative54900-367002015-01-25Multi-vehicle CollisionSide CollisionTotal LossFireNYArlington8758 5th St173?00NO514805720572040040ToyotaHighlander20130NaN
7108292374182007-12-04IN500/100010001337.920441536FEMALEPhDarmed-forcesbungie-jumpingnot-in-family7140002015-02-23Multi-vehicle CollisionSide CollisionMinor DamageAmbulanceWVColumbus7684 Francis Ridge43NO22YES6138011160558044640SuburuLegacy20120NaN
846413150412010-11-02OH100/3002000998.190611556FEMALEMDpriv-house-servvideo-gameshusband43700-663002015-01-23Multi-vehicle CollisionRear CollisionTotal LossAmbulanceSCHillsdale5779 2nd Lane233NO13?78600131001965045850DodgeRAM20041NaN
9286415075451998-12-07IL250/50010001298.856000000435967FEMALEHigh Schoolsalescampingother-relative71300-703002015-01-04Multi-vehicle CollisionFront CollisionMajor DamageAmbulanceNYColumbus6859 Flute Ridge163?13YES5445060501210036300Saab92x20070NaN
months_as_customeragepolicy_numberpolicy_bind_datepolicy_statepolicy_cslpolicy_deductablepolicy_annual_premiumumbrella_limitinsured_zipinsured_sexinsured_education_levelinsured_occupationinsured_hobbiesinsured_relationshipcapital-gainscapital-lossincident_dateincident_typecollision_typeincident_severityauthorities_contactedincident_stateincident_cityincident_locationincident_hour_of_the_daynumber_of_vehicles_involvedproperty_damagebodily_injurieswitnessespolice_report_availabletotal_claim_amountinjury_claimproperty_claimvehicle_claimauto_makeauto_modelauto_yearfraud_reported_c39
690156373716351991-10-13OH500/100010001086.486000000444903MALEAssociatemachine-op-inspcthikingunmarried0-538002015-01-16Multi-vehicle CollisionRear CollisionTotal LossOtherNYNorthbrook8064 4th Ave173?11YES7744014080704056320SuburuLegacy19990NaN
691223407296341994-04-28IN100/3005001201.410437818FEMALEJDpriv-house-servmovieshusband88400-465002015-01-27Multi-vehicle CollisionSide CollisionMajor DamagePoliceNCColumbus7466 MLK Ridge73YES10?70510128201282044870SuburuForrestor19990NaN
692243433675952006-02-03IN500/10005001307.740466137FEMALEAssociatemachine-op-inspctboard-gamesown-child0-757002015-01-28Multi-vehicle CollisionFront CollisionMajor DamageAmbulanceSCRiverwood6451 1st Hwy103?01NO375304170417029190JeepWrangler20080NaN
693275454037371991-12-06IN500/100020001447.770605756FEMALEAssociateadm-clericalcampingwife39400-639002015-01-18Multi-vehicle CollisionSide CollisionTotal LossAmbulanceVANorthbend3835 5th Ave83YES11?6432053601072048240AccuraMDX19980NaN
69488253328922007-10-25IN250/50010001194.000613583FEMALEJDhandlers-cleanersmovieshusband002015-02-13Single Vehicle CollisionRear CollisionMajor DamageAmbulanceSCNorthbrook3982 Washington Hwy61YES12YES667807420742051940FordEscape20131NaN
695199379828711997-07-27IN250/5005001262.080474615MALEJDtech-supportvideo-gameswife4850002015-01-08Single Vehicle CollisionFront CollisionMajor DamageAmbulanceNCColumbus3422 Flute St41?03NO60170109401094038290NissanPathfinder20111NaN
696232437519052001-05-16OH250/5005001483.918000000431531MALECollegemachine-op-inspctgolfhusband0-336002015-01-18Multi-vehicle CollisionRear CollisionMajor DamageAmbulanceNYArlington2318 Washington Hwy173NO01?7060070601412049420VolkswagenPassat20131NaN
697215377611892002-12-28IN100/3005001632.930614417FEMALECollegetransport-movinggolfnot-in-family0-429002015-02-23Multi-vehicle CollisionRear CollisionMinor DamageFireSCRiverwood7923 Elm Ave73NO20YES571209520476042840MercedesC30020020NaN
698270447015212003-07-05IL500/100020001030.950435985FEMALEAssociatemachine-op-inspctpaintballother-relative4720002015-02-03Multi-vehicle CollisionRear CollisionMajor DamageAmbulanceNCNorthbend2865 Maple Lane203?10NO359007180359025130AudiA320071NaN
699269455962981996-08-23IN500/10005001330.460435552FEMALEHigh Schoolmachine-op-inspctsleepingwife54800-641002015-01-18Multi-vehicle CollisionSide CollisionTotal LossPoliceVAHillsdale2220 1st Lane53?00NO242002200440017600SuburuForrestor20080NaN